[Effects of region of interest settings in signal-to-noise ratio measurement of clinical images using arrays of multiple receiver coils].

In many clinical imaging procedures using arrays of multiple receiver coils, a uniform sensitivity process is performed using the sensitivity distribution from the body coil. This causes the noise to be uneven, and background noise cannot be used when measuring the signal-to-noise ratio (SNR). The SNR of clinical images with sensitivity correction using arrays of multiple receiver coils sets the region of interest (ROI) in the region where the signal is uniform, and is limited to the identical ROI method where measurements are taken with noise from the identical region. When SNR is measured with the identical ROI method, uneven noise caused by sensitivity correction as well as the signal strength distribution within the ROI of the object is reflected in the noise. Therefore, evaluation must be performed in as localized a position as possible. Measurement error becomes small on images with higher resolution, and if ROI larger than 10×10 pixels can be set in a region of even signal, SNR measurement of clinical images with less underestimation may be possible.

[Signal-to-noise ratio measurement in parallel MRI with subtraction mapping and consecutive methods].

When measuring the signal-to-noise ratio (SNR) of an image the used parallel magnetic resonance imaging, it was confirmed that there was a problem in the application of past SNR measurement. With the method of measuring the noise from the background signal, SNR with parallel imaging was higher than that without parallel imaging. In the subtraction method (NEMA standard), which sets a wide region of interest, the white noise was not evaluated correctly although SNR was close to the theoretical value. We proposed two techniques because SNR in parallel imaging was not uniform according to inhomogeneity of the coil sensitivity distribution and geometry factor. Using the first method (subtraction mapping), two images were scanned with identical parameters. The SNR in each pixel divided the running mean (7 by 7 pixels in neighborhood) by standard deviation/radical2 in the same region of interest. Using the second (consecutive) method, more than fifty consecutive scans of the uniform phantom were obtained with identical scan parameters. Then the SNR was calculated from the ratio of mean signal intensity to the standard deviation in each pixel on a series of images. Moreover, geometry factors were calculated from SNRs with and without parallel imaging. The SNR and geometry factor using parallel imaging in the subtraction mapping method agreed with those of the consecutive method. Both methods make it possible to obtain a more detailed determination of SNR in parallel imaging and to calculate the geometry factor.

[Method of SNR determination using clinical images].

Parallel magnetic resonance imaging (MRI) with arrays of receiver coils such as those of sensitivity encoding (SENSE) are being widely used. However, conventional methods of image signal-to-noise ratio (SNR) determination cannot be used for parallel MRI, and a novel method has been reported in JSRT. However, this method of SNR determination is for phantom images not for clinical images. Therefore, we researched accurate measurement of the image noise of parallel MRI reconstruction including the unfolding process and uniformity filters. The possibility of a subtraction method using clinical images and the accuracy of standard deviation (SD) of clinical images for optimum ROI were studied because it was not possible to use the air-signal method. The results indicated that the position of the ROI was selected for uniformity of signal intensity area and that the size of the ROI was about 50 pixels. However, under these conditions, the noise value of SNR was higher than that using the phantom-subtraction method. In addition, the tissue-subtraction method was useful when the two scanning images were in agreement.